2024 DataOps Predictions - Part 2

Industry experts offer predictions on how DataOps will evolve and impact IT and business in 2024 ...

Start with: 2024 DataOps Predictions - Part 1

Predictive Analytics

Making decisions based on gut instinct is a thing of the past as organizations are fully realizing the power of analytics to make data-driven decisions, evidenced by the number of software platforms incorporating embedded analytics. Analytics will be all encompassing in 2024 as we become reliant on data for everything from everyday business research such as inventory and purchasing to predictive analytics that allow businesses to see into the future. Predictive analytics will drive businesses forward by helping them make informed, data-driven decisions, improve productivity, and increase sales/revenue — rather than merely reacting in response to events that have already taken place.
Casey Ciniello
Reveal and Slingshot Senior Product Manager, Infragistics

Edge analytics

Edge analytics will be a transformative force as the industry seeks to overcome the challenges posed by the explosive growth of data from interconnected devices and the advancements in AI. Data is not created equally; therefore, it should not be processed that way. Edge analytics processes data locally (at the edge) to quickly filter and preserve the essential information for more insight decisions in real time. In the coming years, this solution will become an integral part of all industries. The industry is already growing quickly, with Allied Market Research projecting that the edge analytics market will reach $47.4 billion by 2030, so business adoption is vital to have a competitive edge. Industries like healthcare, manufacturing, and transportation are already benefiting from edge analytics, and soon it will be necessary for all sectors that are looking for predictive maintenance, reduced costs, operational improvements, and live insights. Because of this growth, the marketplace for edge analytics will also thrive, with many compatible solutions introduced to offer tailored options that cater to the unique needs and use cases of all businesses.
Dia Ali
Global Solutions Leader, Data Intelligence, Hitachi Vantara

Decision Intelligence

One of businesses' biggest challenges in 2024 will be transforming reams of raw data into meaningful insights that can guide decisions that can make or break a company. The journey from insights to decision intelligence marks a transformative era in how organizations harness the raw power of data. We are gradually seeing the shift to decision intelligence as businesses acquire skills, adopt new tools, and most of all, embrace a data-driven mindset. With the help of decision intelligence, organizations will be empowered to make more effective, efficient, and responsible decisions by integrating data, technology, and human expertise. As data continues to shape the future, decision intelligence remains essential for success.”
Casey Ciniello
Reveal and Slingshot Senior Product Manager, Infragistics

First party data

Whether you are trying to feed data into an LLM or simply trying to get ahead of the depreciation of third-party cookies, being able to securely access and harness first-party data is going to be instrumental to how businesses reach their customers as well as build intelligent features and automated workflows in 2024. The reality is that organizations are sitting on a mountain of unstructured customer data within users inboxes. Rather than pour money into spotty third-party insights or outsourcing machine learning models, companies can simply look inward at the data they already own and look to analyze and translate first-party data into positive business outcomes.
Christine Spang
Co-Founder and CTO, Nylas

Data Fabrics

The adoption of ML and AI-enabled Data Fabrics is driven by the explosion and complexity of data. Data Fabrics will be essential in an Industry v4.0 future, with adoption targeted for 2024 and beyond to handle the required data complexity, management, and automation. Utilizing Data Fabrics will make it easier to enforce GDPR compliance, data masking, and other measures. By 2024, 25% of data management vendors are expected to provide a complete framework for data fabric, up from 5% today (Gartner).
Avishai Sharlin
GM, Amdocs Technology

Data Lakes

While some companies may choose to collect less data, increasing regulatory requirements mean that most teams have no choice but to do more with less. As they struggle to find cost-effective means to store data of unpredictable value, companies are increasingly reconsidering data lakes. Once considered the final resting place for unstructured data, I see the migration to data lakes accelerating in 2024, driven by increasing storage costs, as well as advancements in query capabilities across data lakes and object storage, and the comparative ease with which data can be routed into them. With the ability to quickly and cost-effectively search large data stores, companies will start using data lakes as a first stop, rather than a final destination for their data. This will cause a shift of data volumes out of analytics platforms and hot storage into data lakes.
Nick Heudecker
Senior Director, Market Strategy & Competitive Intelligence, Cribl

Data Provenance

In 2024, the digital landscape will continue to see exponential growth in processing power and AI capability. This heightens the need for immutable audit trails and long-term data integrity to establish data provenance. Over the past 12 months, we saw gen AI explode and ultimately expose a massive data provenance problem. 2024 looks to be the year that data provenance becomes more important than ever, and this will be key for AI safety. As trust is the fuel of the digital world, businesses must prioritize transparency, safety, provenance, and accountability. Immutable audit trails not only provide a missing safeguard for data but also provide a verifiable history of every event and transaction in its journey, ensuring trust and reliability in an increasingly interconnected and data-driven environment. With the rise of gen AI, the need for provenance only grows as AI accelerates in capability. Data provenance is essential technology that everybody needs. But we won't need 1,000 different ways to provide long-term integrity for provenance metadata. Next year we will see standards emerge that support open and interoperable methods needed across the internet. There are groups at the IETF and ISO and other communities like the Content Authenticity Initiative that are putting the foundations in place that will provide these capabilities and we should expect companies broadly to adopt and apply them.
Rusty Cumpston
CEO, DataTrails

Data Governance Shifts Left

Data governance will "shift left" as companies collect more data for generative AI. As businesses collect larger volumes of data for their AI initiatives, they must add a governance layer to make the data useful. It's much easier and more efficient to add governance when data is collected, and we will see data governance shift left next year to accommodate this need. Governance investments are critical as they ensure data is reliable and can be made available quickly for use in applications. This governance includes recording the provenance of data, ensuring it is accurate, adding metadata to make it easier to work with, and including it in a catalog so teams know it's available. Storing unstructured and ungoverned data in a data lake makes it easier to save everything, but it becomes progressively more expensive to use any of this data. Companies must work smarter and shift processing to the left as much as possible. This has several benefits. Adding governance sooner means the data is available more quickly, so developers can work with more timely data. It also allows an organization to discard data without future value, reducing storage costs and liability. In 2024, more companies will recognize these benefits and apply data governance earlier.
Andrew Sellers
Head of Technology Strategy, Confluent

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AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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2024 DataOps Predictions - Part 2

Industry experts offer predictions on how DataOps will evolve and impact IT and business in 2024 ...

Start with: 2024 DataOps Predictions - Part 1

Predictive Analytics

Making decisions based on gut instinct is a thing of the past as organizations are fully realizing the power of analytics to make data-driven decisions, evidenced by the number of software platforms incorporating embedded analytics. Analytics will be all encompassing in 2024 as we become reliant on data for everything from everyday business research such as inventory and purchasing to predictive analytics that allow businesses to see into the future. Predictive analytics will drive businesses forward by helping them make informed, data-driven decisions, improve productivity, and increase sales/revenue — rather than merely reacting in response to events that have already taken place.
Casey Ciniello
Reveal and Slingshot Senior Product Manager, Infragistics

Edge analytics

Edge analytics will be a transformative force as the industry seeks to overcome the challenges posed by the explosive growth of data from interconnected devices and the advancements in AI. Data is not created equally; therefore, it should not be processed that way. Edge analytics processes data locally (at the edge) to quickly filter and preserve the essential information for more insight decisions in real time. In the coming years, this solution will become an integral part of all industries. The industry is already growing quickly, with Allied Market Research projecting that the edge analytics market will reach $47.4 billion by 2030, so business adoption is vital to have a competitive edge. Industries like healthcare, manufacturing, and transportation are already benefiting from edge analytics, and soon it will be necessary for all sectors that are looking for predictive maintenance, reduced costs, operational improvements, and live insights. Because of this growth, the marketplace for edge analytics will also thrive, with many compatible solutions introduced to offer tailored options that cater to the unique needs and use cases of all businesses.
Dia Ali
Global Solutions Leader, Data Intelligence, Hitachi Vantara

Decision Intelligence

One of businesses' biggest challenges in 2024 will be transforming reams of raw data into meaningful insights that can guide decisions that can make or break a company. The journey from insights to decision intelligence marks a transformative era in how organizations harness the raw power of data. We are gradually seeing the shift to decision intelligence as businesses acquire skills, adopt new tools, and most of all, embrace a data-driven mindset. With the help of decision intelligence, organizations will be empowered to make more effective, efficient, and responsible decisions by integrating data, technology, and human expertise. As data continues to shape the future, decision intelligence remains essential for success.”
Casey Ciniello
Reveal and Slingshot Senior Product Manager, Infragistics

First party data

Whether you are trying to feed data into an LLM or simply trying to get ahead of the depreciation of third-party cookies, being able to securely access and harness first-party data is going to be instrumental to how businesses reach their customers as well as build intelligent features and automated workflows in 2024. The reality is that organizations are sitting on a mountain of unstructured customer data within users inboxes. Rather than pour money into spotty third-party insights or outsourcing machine learning models, companies can simply look inward at the data they already own and look to analyze and translate first-party data into positive business outcomes.
Christine Spang
Co-Founder and CTO, Nylas

Data Fabrics

The adoption of ML and AI-enabled Data Fabrics is driven by the explosion and complexity of data. Data Fabrics will be essential in an Industry v4.0 future, with adoption targeted for 2024 and beyond to handle the required data complexity, management, and automation. Utilizing Data Fabrics will make it easier to enforce GDPR compliance, data masking, and other measures. By 2024, 25% of data management vendors are expected to provide a complete framework for data fabric, up from 5% today (Gartner).
Avishai Sharlin
GM, Amdocs Technology

Data Lakes

While some companies may choose to collect less data, increasing regulatory requirements mean that most teams have no choice but to do more with less. As they struggle to find cost-effective means to store data of unpredictable value, companies are increasingly reconsidering data lakes. Once considered the final resting place for unstructured data, I see the migration to data lakes accelerating in 2024, driven by increasing storage costs, as well as advancements in query capabilities across data lakes and object storage, and the comparative ease with which data can be routed into them. With the ability to quickly and cost-effectively search large data stores, companies will start using data lakes as a first stop, rather than a final destination for their data. This will cause a shift of data volumes out of analytics platforms and hot storage into data lakes.
Nick Heudecker
Senior Director, Market Strategy & Competitive Intelligence, Cribl

Data Provenance

In 2024, the digital landscape will continue to see exponential growth in processing power and AI capability. This heightens the need for immutable audit trails and long-term data integrity to establish data provenance. Over the past 12 months, we saw gen AI explode and ultimately expose a massive data provenance problem. 2024 looks to be the year that data provenance becomes more important than ever, and this will be key for AI safety. As trust is the fuel of the digital world, businesses must prioritize transparency, safety, provenance, and accountability. Immutable audit trails not only provide a missing safeguard for data but also provide a verifiable history of every event and transaction in its journey, ensuring trust and reliability in an increasingly interconnected and data-driven environment. With the rise of gen AI, the need for provenance only grows as AI accelerates in capability. Data provenance is essential technology that everybody needs. But we won't need 1,000 different ways to provide long-term integrity for provenance metadata. Next year we will see standards emerge that support open and interoperable methods needed across the internet. There are groups at the IETF and ISO and other communities like the Content Authenticity Initiative that are putting the foundations in place that will provide these capabilities and we should expect companies broadly to adopt and apply them.
Rusty Cumpston
CEO, DataTrails

Data Governance Shifts Left

Data governance will "shift left" as companies collect more data for generative AI. As businesses collect larger volumes of data for their AI initiatives, they must add a governance layer to make the data useful. It's much easier and more efficient to add governance when data is collected, and we will see data governance shift left next year to accommodate this need. Governance investments are critical as they ensure data is reliable and can be made available quickly for use in applications. This governance includes recording the provenance of data, ensuring it is accurate, adding metadata to make it easier to work with, and including it in a catalog so teams know it's available. Storing unstructured and ungoverned data in a data lake makes it easier to save everything, but it becomes progressively more expensive to use any of this data. Companies must work smarter and shift processing to the left as much as possible. This has several benefits. Adding governance sooner means the data is available more quickly, so developers can work with more timely data. It also allows an organization to discard data without future value, reducing storage costs and liability. In 2024, more companies will recognize these benefits and apply data governance earlier.
Andrew Sellers
Head of Technology Strategy, Confluent

Related Links

Hot Topics

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...